[search]
    n_live_points -> int
        The number of live points used to sample non-linear parameter space. More points provides a more thorough
        sampling of parameter space, at the expense of taking longer to run. The number of live points required for
        accurate sampling depends on the complexity of parameter space, below is a rough guideline of how many to use.

        for ~5 non-linear parameters -> 20-40 live points
        for ~10 non-linear parameters -> 30-50 live points
        for ~20 non-linear parameters -> 50-100 live points
        for 25+ non-linear parameters -> 75+ live points
    sampling_efficiency -> float
        The ratio of accepted to total samples MultiNest targets. A higher efficiency will converges on the high
        log_likelihood regions of parameter space faster at the risk of missing the global maxima solution. By default we
        recommend a value of 0.8 (without constant efficiency mode) and 0.3 (with constant efficiency mode). Reduce
        to lower values if the inferred solution does not look accurate.
    const_efficiency_mode -> bool
        The sampling efficiency determines the acceptance rate MultiNest targets. However, if MultiNest cannot map out
        parameter-space accurately it reduce the acceptance rate. Constant efficiency mode forces MultiNest to maintain
        the sampling efficiency acceptance rate. This can dramatically reduce run-times but increases the risk of missing
        the global maximum log likelihood solution.
    evidence_tolerance -> float
        MultiNest will stop sampling when it estimates that continuing sampling will not increase the log evidence more than
        the evidence_tolerance value. Thus, the higher the evidence_tolerance the sooner MultiNest will stop running. Higher
        tolerances provide more accurate parameter errors.
    multimodal -> bool
        Whether MultiNest uses multi-modal sampling, whereby the parameter space search will 'split' into multiple modes
        if it detects there are multiple peaks in log_likelihood space.
    importance_nested_sampling -> bool
        Importance nested sampling mode uses information from the rejected points to improve the non-linear search.
    max_modes -> int
        If multimodal sampling is True, the maximum number of models MultiNest can split into.
    mode_tolerance -> float
        MultiNest can find multiple modes & also specify which samples belong to which mode. It might be desirable
        to have separate samples & mode statistics for modes with local log-evidence value greater than a
        particular value in which case Ztol should be set to that value. If there isn’t any particularly
        interesting Ztol value, then Ztol should be set to a very large negative number (e.g. -1e90).
    max_iter -> int
        maximum number of iterations. 0 is unlimited.
[settings]
    n_iter_before_update -> int
        Number of accepted samples (times 10) per MultiNest output to hard disk.
    null_log_evidence -> float
        If multimodal is True, MultiNest can find multiple modes & also specify which samples belong to which mode.
        It might be desirable to have separate samples & mode statistics for modes with local log-evidence value
        greater than a particular value in which case nullZ should be set to that value. If there isn’t any
        particulrly interesting nullZ value, then nullZ should be set to a very large negative number (e.g. -1.d90).
    seed -> int
        The random number generator seed of MultiNest, enabling reproducible results.
    verbose -> bool
        Whether MultiNest prints messages.
    resume -> bool
        If `True` and existing results are found at the output path, MultiNest will resume that run. If False,
        MultiNest will start a new run.
    context -> None
        Not used by PyAutoFit.
    write_output -> bool
        Whether the results are written to the hard-disk as text files (allowing the run to be resumed).
    log_zero -> float
        points with loglike < logZero will be ignored by MultiNest.
    init_MPI -> None
        MPI not supported by PyAutoFit for MultiNest.

Nest.ini